text: import argparse
  import os
  
  import tensorflow as tf
  
  from mmtgan.train import train as mmtgan_train
  from mtgan.train import train as mtgan_train
  from srgan.train import train as srgan_train
  
  tf.compat.v1.disable_eager_execution()
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
  
      parser.add_argument('-i', '--input_dir', action='store', dest='input_dir',
                          default='./data',
                          help='Training images directory path.')
  
      parser.add_argument('-l', '--plate_labels', action='store',
                          dest='plate_labels',
                          help='File container license plates labels')
  
      parser.add_argument('-n', '--network', action='store', dest='network',
                          help='Network to train.',
                          choices=['srgan', 'mtgan', 'mmtgan'])
  
      parser.add_argument('-o', '--output_dir', action='store', dest='output_dir',
                          default='./output',
                          help='Train examples output directory path.')
  
      parser.add_argument('-c', '--checkpoint_dir', action='store',
                          dest='checkpoint_dir', default='./checkpoints',
                          help='Path to save model checkpoints.')
  
      parser.add_argument('-b', '--batch_size', action='store', dest='batch_size',
                          default=64,
                          help='Images batch size.', type=int)
  
      parser.add_argument('-e', '--epochs', action='store', dest='epochs',
                          default=1000,
                          help='Epoch for training the network.', type=int)
  
      values = parser.parse_args()
  
      output_dir = values.output_dir
      checkpoint_dir = values.checkpoint_dir
  
      os.makedirs(output_dir, exist_ok=True)
      os.makedirs(checkpoint_dir, exist_ok=True)
  
      if values.network == 'srgan':
          srgan_train(
              values.epochs, values.batch_size, values.input_dir, output_dir,
              checkpoint_dir)
      elif values.network == 'mtgan':
          mtgan_train(
              values.epochs, values.batch_size, values.input_dir,
              values.plate_labels, output_dir, checkpoint_dir)
      elif values.network == 'mmtgan':
          mmtgan_train(
              values.epochs, values.batch_size, values.input_dir,
              values.plate_labels, output_dir, checkpoint_dir)
  